Data Science Journal
The Data Science Journal was relaunched in 2014 in partnership with Ubiquity Press. For more information about the Data Science Journal, to submit an article or to get involved please visit the Data Science Journal website with Ubiquity Press: http://datascience.codata.org/
A New Editor-in-Chief
Dr. Sarah Callaghan, of the British Atmospheric Data Centre, and previously a co-chair of a number of CODATA Task Groups, including the TG on Data Citation, has been appointed as the first Editor-in-Chief of the next generation Data Science Journal.
Talk of a ‘data revolution’ is not hyperbole. Recent decades have seen an unprecedented explosion in the human capacity to acquire, store and manipulate data and information. It is a world historical event involving a revolution in knowledge creation, communication and utilisation as profound as and more pervasive than that associated with Gutenberg’s invention of the printing press. These developments involve profound transformations in the conduct of research. They raise issues that affect science policy, the conduct and methods of research and the data systems, standards and infrastructure that are integral to research. The evidence-based study of these things is Data Science.
The Data Science Journal is dedicated to the advancement of data science and its application in policies, practices and management as Open Data to ensure that data are used in the most effective and efficient way in promoting knowledge and learning. It is a peer-reviewed, open access, electronic journal that is relevant to the whole range of computational, natural and social science and the humanities. The scope of the journal includes descriptions of data systems, their implementations and their publication, applications, infrastructures, software, legal, reproducibility and transparency issues, the availability and usability of complex datasets, and with a particular focus on the principles, policies and practices for data.
All data is in scope, whether born digital or converted from other sources, and all research disciplines are covered. Data is a cross-domain, cross-discipline topic, with common issues, regardless of the domain it serves.
The Data Science Journal publishes a variety of article types (research articles, practice papers, review articles and essays). The Data Science Journal also publishes data articles, describing datasets or data compilations, if the potential for reuse of the data is significant or if considerable efforts were required in compilation. Similarly, the Data Science Journal also publishes descriptions of online simulation, database, and other experiments, partnering with digital repositories on ‘meta articles’ or ‘overlay articles’, which link to and allow visualisation of the data, thereby adding an entirely new dimension to the communication and exchange of data research results and educational materials.